Method for adaptively selecting ground penetrating radar image for detecting moisture damage

ABSTRACT

A method for adaptively selecting a ground penetrating radar (GPR) image for detecting a moisture damage is provided. The method adaptively selects the GPR image according to a contrast of the GPR image. The method includes the following steps: S 1,  reading pre-processed GPR data; S 2,  adjusting a resolution of a picture; S 3,  inputting a data set into a recognition model; S 4,  outputting a moisture damage result; S 5,  judging whether there is a detection target or not by using an initial random image data set; and S 6,  generating the GPR image randomly incrementally and selecting the GPR image with a proper contrast. A proper B-scan image is found effectively, quickly and automatically by combining a recognition algorithm and a deep learning model or an image classification model to achieve an automatic recognition and detection based on the GPR image and improving a recognition precision as well.

CROSS REFERENCE TO THE RELATED APPLICATIONS

This application is the national stage entry of InternationalApplication No. PCT/CN2020/120598, filed on Oct. 13, 2020, which isbased upon and claims priority to Chinese Patent Application No.201911059296.3 filed on Nov. 1, 2019, the entire contents of which areincorporated herein by reference.

TECHNICAL FIELD

The present invention belongs to the field of automatic detection,relates to a ground penetrating radar image (or B-scan image), inparticular to a method for adaptively selecting a ground penetratingradar image for detecting a moisture damage.

BACKGROUND

Ground penetrating radar (Ground Penetrating Radar, GPR for short) is aninstrument for detecting a condition under the surface of earth andimaging by radar impulse waves, and its principle is detecting amaterial characteristic in a medium by emitting and receiving highfrequency electromagnetic (EM) waves via an antenna. GPR uses a highfrequency wireless radio wave which is usually polarized, the EM wave isemitted under the surface of the earth, and when the EM wave strikes anobject buried under the surface of the earth or reaches a boundary withvariable dielectric constants, a reflected wave received by the receiveantenna will record a signal difference of a reflection echo. Featuredwith high precision, high efficiency and non-destructive in detection byground penetrating radar, GPR is widely applied to many fields ofarchaeology, mineral exploration, hazardous geological survey,geotechnical engineering investigation, engineering quality detection,building structure detection and military target detection.

As GPR can be used for continuous, fast and non-destructive detection,GPR has already been applied to road traffic, for example, GPR has beenwidely applied to cavitation of a tunnel substrate, pavement cavitation,recognition of dowel steel or a reinforcing bar of a building or abridge deck slab and underground pipelines. However, it is hard toexplain ground penetrating radar data, explanation of the GPR image isoften dependent on experience of a GPR expert. Furthermore, in an actualproject, there are many pavement structures and many defect types, anddepths where defects are distributed are different, for example, thedefects of the asphalt pavement primarily include moisture damage,crushing and cracks centralized in an asphalt layer (the depth rangesfrom 0 to 22 cm). The defect of a cement pavement is primarilycavitation centralized in a semi-rigid layer (the depth is greater than24 cm). A white and black pavement has the two defects and the depthranging from the asphalt layer, the cement pavement, the semi-rigidlayer and a roadbed are 0-1.5 m. In order to detect different defects,it is often needed to combine radar antenna in an array. Meanwhile, as aresult of highway mileage, pavement inspection will generate mass data.It is hard to meet an intelligent detection demand on the pavementdependent on an artificial experience recognition method. It is anurgent need to establish a database of pavement defects and to introduceintelligent recognition methods such as deep learning to detect defectsof an expressway (or other underground target bodies) intelligently.

In existing related researches, GPR images and a deep learning methodare combined for automatic recognition. At present, related researchesmainly focus on recognizing hyperbola targets (such as isolated targetbodies, for example, a reinforcing steel bar, a crack and the like). Adeep learning model is adopted to recognize and position hyperbolafeatures of the radar image to achieve a relatively high recognitionprecision. A data set is constructed by optimizing GPR images with highcontrast between target and ground dependent on expertise. In mostcases, GPR data (A-SCAN signal, is single radar trace, or waveform) issubjected to signal post processed methods such as DC offset removal,gain processing, band-pass filtering, background removing,two-dimensional filtering, migration and deconvolution, the target bodyfeature is highlighted from background and then the GPR image (B-SCAN,stacking of A-Scans) is intercepted to construct a proper GPR image dataset. On this basis, training of a corresponding deep learning model (oran image classification model) improves the automatic recognitionefficiency and precision of the GPR images, thereby providing a novelanalytical method for automatic application of GPR.

However, in an existing deep learning study for recognizing the targetbody based on the radar images, contrasts (plot scale) of all GPR imagesare selected according to manual experience and are centralized in thehyperbola target with obvious features. The GPR images with suitableplot scales or contrast values can facilitate an expert or the deepmodel to detect the target body correctly and the images withunreasonable contrast value will gain a mistaken error. Intelligentdetection application of GPR is hindered by selecting the GPR imagesmanually.

Taking a common pavement defect as an example, the moisture damage isone of major reasons which cause early damage of the asphalt pavement.Quick detection of the moisture damage is a difficulty all the time. TheChinese patent (Method for recognizing moisture damage based ontime-frequency statistic characteristic of ground penetrating radarsignal, 201910100046.3) provided by the writer achieves detection andautomatic analysis of the moisture damage, analyzed from the aspect ofGPR data rather than spectra. GPR images is an effective method forjudging the moisture damage and the bridge joint. The defect of themoisture damage is not the hyperbola feature but a more complex imagefeature. There are no related detection studies utilizing the images.

As a result of different moisture contents of the defects, in detectionby a same set of GPR apparatus on a same path at one time, the plotscales corresponding to the proper images in different moisture damageregions are different, which hinders application of GPR in automaticdetection. Therefore, it is an urgently need to provide a method forselecting the radar image with the proper contrast automaticallyaccording to the GPR data and then inputting the GPR image into therecognition model (for example the deep model) to recognize the targetbody (moisture damage) automatically.

SUMMARY

Aiming at deficiencies and defects in the prior art, the presentinvention aims to provide a method for adaptively selecting a groundpenetrating radar image for detecting a moisture damage, which solves aproblem of selecting the GPR image with the proper contrast valuemanually based on the GPR image in the prior art.

In order to solve the technical problem, the present invention adopts atechnical scheme as follows:

A method for adaptively selecting a ground penetrating radar image fordetecting a moisture damage, wherein the method adaptively selects aground penetrating radar image with a proper contrast according to dataof the GPR image, the method including the following steps:

S1, reading pre-processed GPR data:

generating radar images with different contrast values randomly in a setcontrast data range after pre-processing GPR data, so as to construct aninitial random image data set, the initial random image data setincluding N pictures;

S2, adjusting resolutions of the pictures:

defining the initial random image data set as an RID data set, zoomingthe RID data set to 224*224 and defining the zoomed data set as a RBDdata set;

zooming the resolution of a moisture damage initial image data setdirectly to 224*224 to obtain the RBD data set;

S3, inputting the data set into a recognition model:

inputting the RBD data set obtained in the step S2 into the recognitionmodel, and executing a step S4 after operation of the recognition model,

wherein the picture input resolution size of the recognition model is224*224 and the picture output resolution size of the recognition modelis 224*224;

the recognition model is a mixed deep learning model, the mixed deeplearning model is comprised of two portions: feature extraction adoptingResNet50 and target detection adopting a YOLO V2 frame;

S4, outputting a moisture damage result:

post-processing the output result of the recognition model in the S3,post-processing including the following steps:

S41, judging the output quantity of candidate boxes BBoxes of images,executing S42 if the quantity of candidate boxes BBoxes is greater than1, otherwise, outputting a result directly;

S42, judging whether the candidate boxes BBoxes are overlapped or not,executing S43 if the candidate boxes BBoxes are overlapped, otherwise,outputting the result directly;

S43, judging whether label names corresponding to the overlappedcandidate boxes are identical or not, and if yes, the label namescorresponding to the merged candidate boxes being invariable and if no,indicating that moisture damage label names and bridge joint label namesare comprised simultaneously, the label names being output as bridgejoint, Joint;

S44, merging the candidate boxes, taking the minimum value ofintersected candidate boxes in x and y directions, taking the maximumvalue of w and h, and coordinates of the merged candidate boxes being[xmin, ymm, wmax, hmax];

S45, outputting the result, adjusting the output picture resolution tobe equal to a picture resolution of the damage initial image data set inthe output result of the recognition model, the output result being thelabel name with a target and an image of the candidate box BBoxes (x, y,w, h) corresponding to the target;

S5, judging whether a detection target is present or not with an initialrandom image data set:

S51, converting the output result BBoxes in the S4 into a matrix A_(i)corresponding to pixel points in the picture, A_(i) being defined as:

${A_{i}\left\lbrack {m,n} \right\rbrack} = \left\{ {\begin{matrix}{1,\ } & {x_{i} \leq m \leq {x_{i} + {W_{i}\ {and}\ y_{i}}} \leq n \leq {y_{i} + h_{i}}} \\{0,\ } & {other}\end{matrix},} \right.$

where 1≤m≤H₀, 1≤n≤W₀ where in H₀ is a picture height of the image outputby the recognition model and W₀ is a picture width of the image outputby the recognition model;

summating the matrixes A_(i) corresponding to the N pictures in the RIDdata set and calculating a mean value thereof to acquire a mean valuematrix A, A being defined as:

${A = {\frac{1}{N}{\sum\limits_{i = 1}^{N}A_{i}}}};$

S52, setting k₁=0.8 and θ₀=0.5, and updating the mean value matrix Aaccording to a formula below to acquire an updated mean value matrix A,set the elements in A which are lower than the k₁*max(max(A)) θ₀), to 0and up date A;

A(A<max(k ₁*max(max(A)), θ₀))=0

wherein

k₁ is a target association coefficient;

θ₀ is the minimum value in the matrix A if the target is comprised, andno target is present if lower than the value;

max(max(A)) is the maximum value in the mean value matrix;

S53, acquiring a judging condition T for judging whether the target ispresent or not according to a formula below on a basis of the updatedmean value matrix A, and if T is equal to 1, indicating that a target ispresent and if T is equal to 0, indicating that no target is present;

$T = \left\{ {\begin{matrix}{1,\ } & {{{where}\ \max\left( {\max(A)} \right)} > 0} \\{0,\ } & {{{where}\ \max\left( {\max(A)} \right)} = 0}\end{matrix};} \right.$

S6, generating the image randomly incrementally and selecting the imagewith a proper contrast:

when the picture contains the target, performing initial judgment;

S61, if Flag is equal to 0, indicating that the random image sample setis generated for the first time, i.e., an initial sample set stage, notentering follow-up selecting judgment, setting Flag=1, then adding 5% ofN pictures additionally as a sample of the random image data set, thetotal number of the pictures in the sample being N=(1+5%) N, andreturning to the S2;

S62, if Flag is not equal to 0, indicating a non-initial stage, settinga picture association coefficient, and selecting the picture with themaximum association coefficient of the mean value matrix A as the imagewith the proper contrast;

the association coefficient R_(i) is defined as

${R_{i} = \frac{\sum\limits_{m = 1}^{H_{0}}{\sum\limits_{n = 1}^{W_{0}}{\left( {{A\left( {m,n} \right)} - \mu_{A}} \right)\left( {{A_{i}\left( {m,n} \right)} - \mu_{A_{i}}} \right)}}}{\sqrt{\sum\limits_{m = 1}^{H_{0}}{\sum\limits_{n = 1}^{W_{0}}{\left( {{A\left( {m,n} \right)} - \mu_{A}} \right)^{2}{\sum\limits_{m = 1}^{H_{0}}{\sum\limits_{n = 1}^{W_{0}}\left( {{A_{i}\left( {m,n} \right)} - \mu_{A_{i}}} \right)^{2}}}}}}}};$

wherein R_(i) is an association coefficient between the matrix A_(i)corresponding to the i^(th) image and the mean value matrix A; m is acoordinate value in a height direction; n is a coordinate value in awidth direction; μ_(A) is a total mean value of the mean value matrix A;and μ_(A) _(i) is a total mean value of the matrix A_(i);

a termination condition of the selection process is as following:

STOP = abs(F1 − F1_(pre)) < 0.01&&F1 > 0.8;${{F1} = {2*\frac{{Precision} \cdot {Recall}}{{Precision} + {Recall}}}};$${{Precision} = \frac{TP}{{TP} + {FP}}};$${{Recall} = \frac{TP}{{TP} + {FN}}};$

wherein F1 is an evaluation index of deep learning; F1 _(Pre) is anevaluation index of the previous deep learning, and is 0 initially; TPis a true target region; FP is a misrecognized true value, representingthat an unrecognized true value is judged as a negative value or abackground mistakenly; and FN is a misrecognized negative value, i.e.,the background is taken as the target; assigning the current F1 value toa variable F1 _(pre) when the termination condition is not met, and thenreturning to the S61 to increase the sample set so as to re-select thesuitable image;

outputting the image with the proper contrast by the system when thetermination condition is met.

The present invention further has the following technicalcharacteristics:

The method for acquiring GPR data includes: acquiring field data of theasphalt pavement by using the GPR system, determining a damaged regionof the asphalt pavement with stripping or whitening in the field dataacquisition process and acquiring GPR data corresponding to the damagedregion.

During a field data acquisition process, sampling parameter requirementsinclude that a sampling interval is smaller than 15 cm, an antennafrequency is greater than 1.6 GHz and a sampling frequency is 10-20times of a main frequency of an antenna.

Pre-processing is a course of adopting a direct current drift connectionalgorithm, a ground correction algorithm, a background deductionalgorithm, a band-pass filtering algorithm and performing a slidingaverage algorithm to perform pre-processing.

The set data range of the contrast is 0.5-1.8.

N is equal to 100.

Compared with the prior art, the present invention has the benefits that

(I) Based on a concept of particle filtering, to-be-detected GPR data isread and GPR image with different contrast values are generatedrandomly, so that a random data set is constructed by a result. Thegenerated images are feed into the recognition model and a thresholdvalue is set based on a global statistic result. A proper image withsuitable plot-scale value is found on this basis and consistency of theproper image found behind and after is compared as a searching andjudging condition. If a stop condition is not met, a random sample imagedataset size is increased continuously. The research covers image withall possible contrasts, can find the proper B-scan image quickly andeffectively and solves the problem of selecting the proper radar imageautomatically, thereby laying a foundation for recognizing the GPR imageautomatically.

(II) A recognition algorithm and the deep learning model (or the imageclassification model) are combined to find the proper B-scan imageeffectively, quickly and automatically to achieve automatic recognitionand detection based on the GPR spectra, and meanwhile, the recognitionprecision is improved.

(III) Verified by an experiment, the method and the moisture damagedetection model based on YOLO are used for generating the proper GPRspectra from original GPR data and detecting the moisture damage defectautomatically, thereby improving the intelligence of moisture damagedetection of the asphalt pavement.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow diagram of a moisture damage defect detection methodbased on GPR.

FIG. 2 is a typical image feature of the moisture damage defect dataset.

FIG. 3 is a post-processing flow diagram of a recognition model result.

FIG. 4 is an index comparison diagram of a mixed model under differentresolution data sets.

FIG. 5 is a moisture damage detection result diagram under the mixedmodel.

FIG. 6 is a detection result diagram of an ACF algorithm.

FIG. 7 is a detection result diagram by using a Cifar 10 model.

FIG. 8 is GPR image corresponding to different contrast values.

FIG. 9 is a GPR image recognition algorithm with the proper contrast.

FIG. 10 is a change rule of related coefficients dependent on number ofsampled samples.

FIG. 11 is a change rule of related indexes of the deep model dependenton number of sampled samples.

FIG. 12 is a heatmap result for overall detection of a random data setoverlapping on an optimum image.

FIG. 13 is a heatmap result of mean value of matrix A overlapping on anoptimum result

FIG. 14 is a heatmap result for overall detection of the normal pavementand the random data set.

FIG. 15 is a distribution rule of the number of random samples of a testdata set.

FIG. 16 is a comparison result of the algorithm (IRS) and the randomsample (RS) in a moisture damage test set.

FIG. 17 is a comparison result of the algorithm (IRS) and the randomsample (RS) with the normal pavement increasingly.

Implications of the marks in the drawings are as follows: 1-1, GPR imagecorresponding to a proper contrast value, 1-2, GPR image correspondingto a too small contrast value, 1-3, GPR image corresponding to a toolarge contrast value and 1-4, a true moisture damage defect range in theGPR image corresponding to the proper contrast.

Further description of specific embodiments of the present invention indetail will be made below in combination with drawings.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Reasons of contrasts (plot scale) on influence of GPR spectra: asphaltpavement investigation is carried out by using GPR setting to obtainradar data, the radar data is post-processed to increase differencebetween the target body and the background and then the processed radardata is converted into the GPR image. FIG. 8 is the GPR imagecorresponding to different contrasts, wherein 1-1 is a GPR imagecorresponding to the proper contrast, and 1-4 is the moisture damagedefect range detected in the GPR data. 1-2 is a radar imagecorresponding to the too small contrast. But the defect feature in theregion corresponding to 1-4 is not highlighted on the 1-2 image and isnot easily recognized by a GPR expert or model; 1-3 is a GPR imagecorresponding to the too large contrast. Except the region correspondingto 1-4 is displayed as the moisture damage defect, the rest of normalpavement regions are highlighted regions which will be misjudged as thedefect regions. It can be known from analysis of FIG. 8, the contrastvalue or plot scale value is quite important to recognize the GPR imagetarget with the suitable contrast.

Specific embodiments of the present invention are given below. It shouldbe noted that the present invention is not limited to the specificembodiments below and equivalent transformations made based on thetechnical scheme of the application shall fall within the scope ofprotection of the present invention.

Embodiment 1

The embodiment provides a method for detecting a moisture damage of anasphalt pavement as shown in the FIG. 1 to FIG. 3. The method includesthe following steps:

S1, a moisture damage image data set is acquired through GPR fieldsurvey on asphalt pavements:

S11, GPR pavement investigation and data acquisition: field GPR data ofthe asphalt pavement acquired by using the GPR system, and a damagedregion of the pavement with stripping or whitening is determined in thefield data acquisition process;

In the S11, during the field data acquisition process, required bysampling parameters, a sampling interval is smaller than 15 cm, anantenna frequency is greater than 1.6 GHz and a sampling frequency is10-20 times of a main frequency of an antenna;

these marks will emerge above the GPR image in form of small squares. Inthe FIG. 2, the marks of the radar spectra are “□”, and the lower sidesof the marks correspond to the moisture damage defect region. The GPRimages corresponding to the marks as true values of moisture damage areused for determining features of the moisture damage defect;

S12, an initial image set of the moisture damage is acquired: afterpre-processing the GPR data corresponding to the damage region, thecontrast of the GPR image is set and the GPR image is interceptedaccording to a length of 5-6 m to construct the initial image data setof the damage with the moisture damage, the bridge joint and the normalpavement, and features are marked respectively;

the image resolution of the initial image data set of the damage is1090*300;

In the S12, pre-processing is a course of adopting a direct current (DC)drift correction algorithm (DC offset correction), a ground correctionalgorithm (find the ground layer), a background deduction algorithm(subtract the mean value of A-Scans), a band-pass filtering algorithmand performing a sliding average algorithm to perform pre-processing.

in the S12, the contrast of the set GPR image is 1.2-1.6, preferably 1.4in the embodiment.

FIG. 2 is the typical image of the moisture damage defect data set, afield picture is on the left side, the corresponding GPR image is on theright side, and in a label, Moisture is the moisture damage and Joint isthe bridge joint.

A process of acquiring the image data set of the moisture damage: whenpassing through the moisture damage region, a GPR antenna will mark indata acquisition software, and main features of the moisture damage aredetermined by plenty of investigation of living examples:

1) there are continuous or discontinuous highlighted regions in theasphalt layer;

2) the width/Height ratio in the image region is indefinite and ispositively correlated to order of severity of the moisture damage.

The lowermost image in the FIG. 2 is the bridge joint image which ischaracterized in that the bridge joint presents a continuous highlightedregion from the pavement downwards and the continuous highlighted regionis primarily different from the highlighted region of the moisturedamage:

1) The feature is highlighted from the surface of the pavement downwardsand hyperbola features will emerge on two sides;

2) the highlighted region is continuous in feature and the depth fromthe surface to the lower side Depth is greater than or equal to 0.1 m;

3) the Width/Height ratio in the image region is smaller than 4 and thearea Area is greater than 1000 pixel².

S2, resolutions of the pictures are adjusted:

It is found by researches that the images with different resolutions aredifferent in accuracy in the recognition model and the resolutions ofthe pictures affect the model recognition effect directly;

the damaged initial image data set is defined as an RID data set, the IDdata set is directly zoomed to 224*224 and the zoomed data set isdefined as a BD data set;

the resolution of a damaged initial image data set is directly zoomed to224*224 to obtain the BD data set;

S3, the data set is input into the recognition model:

the BD data set acquired in the S2 is input into the recognition model,and S4 is executed after operation by the recognition model;

the picture input resolution size of the recognition model is 224*224and the picture output resolution size of the recognition model is224*224;

the recognition model is a mixed deep learning model, the mixed deeplearning model is comprised of two portions: feature extraction adoptingResNet50 and target detection adopting a YOLO V2 frame;

the ResNet50 and YOLO V2 frames are known deep learning models.

Feature extraction is comprised of four stages to achieve 16-time downsampling to convert the input 224*224 into 14*14*1024, thereby providingCNN original data to follow-up YOLO detection;

In the YOLO v2 frame, a target detection and candidate frame isprovided, YOLO Class Cony is provided with grids Grid=14*14, Anchorboxes=6. Loss function set by YOLO Transform is MSE (Mean SquaredError).

The mixed deep learning model is divided into a training set and a testset by means of the images obtained in the S2, the distributionproportion being 70% and 30%. A specific model training method includestraining the designed mixed deep learning model by using a TL (Transferlearning) method. Loss function of the model uses a MSE method, and thenumber of Anchor boxes is acquired by classifying Height/Width ratios ofthe moisture damage and the bridge joint of the sample set according toa K-means method.

The mixed deep learning model uses three indexes: F1, Recall andPrecision to measure performance of the model.

S4, a moisture damage result is output:

A result given by the recognition model has an overlapping phenomenon,including:

1) a longer moisture damage defect will have a plurality of predictedresults which are overlapped;

2) part of the bridge joints expect a plurality of results judged aremisjudged as moisture damages;

therefore, FIG. 3 is the post-processing flow diagram of the GPR imagewith coordinate axis, specifically including:

the output result of the recognition model in the S3 is post-processed,post-processing including the steps:

S41, the quantity of candidate boxes BBoxes of GPR images in the outputresult is judged, S42 is executed if the quantity of candidate boxesBBoxes is greater than 1, otherwise, a result with no target is outputdirectly;

S42, whether the candidate boxes BBoxes are overlapped or not is judged,S43 is executed if the candidate boxes BBoxes are overlapped, otherwise,the result is output directly;

S43, whether label names corresponding to the overlapped candidate boxesare judged same or not, and if yes, the label names corresponding to themerged candidate boxes being invariable and if no, moisture damage labelnames and bridge joint label names are comprised simultaneously areindicated, the label names being output as the bridge joints;

S44, the candidate boxes are merged, the minimum value of intersectedcandidate boxes in x and y directions is taken, the maximum value of wand h is taken, and coordinates of the merged candidate box being[x_(min), y_(min), x_(max), h_(max)];

S45, the result is output, the output picture resolution is adjusted tobe equal to a picture resolution of the damage initial image data set inthe output result of the recognition model, the output result being thelabel name with a target and an image of the candidate box BBoxes (x, y,w, h) corresponding to the target;

(A) the present invention breaks through detection focused on hyperbolafeature targets in automatic detection in the existing GPR field andachieves automatic detection of moisture damage defects of the asphaltpavement with complex target body features, thereby providing a groundfor precise pre-maintain the asphalt pavement and automatic positioningof the moisture damage defect.

(B) as the present invention considers influence of zooming of theresolutions of the pictures and detects the moisture damage defectsautomatically by using the mixed model, it is time- and labor-wasting torecognize existing moisture damage defects by means of expertise and itis affected by human factors.

(C) the training sample sets of the present invention are originatedfrom field test data and the samples are of wide representativeness, sothat the problem that a FDTD simulation software synthesized data setfor existing GPR field researches is not representative in sample issolved and limitation that automatic recognition in GPR field is onlyfocused on automatic detection of hyperbola features is broken through.

(D) as the method provided by the present invention can achieveautomatic detection and accurate positioning of the moisture damagedefects, the recognition model can be provided for automatic detectionbased on an unmanned inspection vehicle in the later period, therebyachieving periodical detection and inspection in a defect region andfurther achieving precise curing and intelligent pavement maintenance.

COMPARATIVE EXAMPLE 1

The comparative example provides the method for detecting the moisturedamage of the asphalt pavement. Other steps of the method are same asthose in the embodiment 1 and the difference is merely that the S2 isdifferent, and the input images in the S3 are different.

S2, resolutions of the pictures are adjusted:

the damaged initial image data set is defined as an ID data set, the IDdata set is cut according to a dimension of 224*224 and the cut imagesincluding the moisture damages and the bridge joints are defined as anSD data set;

the damaged initial image data set is cut according to the dimension224*224 to obtain the SD data set.

COMPARATIVE EXAMPLE 2

The comparative example provides the method for detecting the moisturedamage of the asphalt pavement. Other steps of the method are same asthose in the embodiment 1 and the difference is merely that the S2 isdifferent, and the input images in the S3 are different.

S2, resolutions of the pictures are adjusted:

the damaged initial image data set is defined as an ID data set, the IDdata set is cut according to a dimension of 224*224 and the cut spectraincluding the moisture damages and the bridge joints are defined as anSD data set, and the spectra constructed by mixing the BD data set andthe SD data set as an MD data set;

the resolution of the damaged initial image data set is adjusted toobtain the MD data set.

Contrastive analysis is performed on the embodiment 1, the comparativeexample 1 and the comparative example 2, 1431 spectra of the originalimage data set is constructed according to the algorithm, and the BD, SDand MD data sets are constructed according to algorithm respectively.FIG. 4 is a result of the training model. It can be known from thefigure that the networks of mixed deep model trained on the data setshave better results on test set, showing that the mixed deep model isfeasible. The model trained on the BD data set is optimum, therecognition precision of the model is F1=91.97%, Recall=94.53% andPrecision=91.00%. Thus, the training model is selected as the BD model,and preferably, a resolution zooming method zooms the original spectradirectly in an equal proportion.

COMPARATIVE EXAMPLE 3

The comparative example provides a method for detecting the moisturedamage of the asphalt pavement. The method detects the moisture damageof the asphalt pavement by using an ACF (Aggregate Channel Features)algorithm.

COMPARATIVE EXAMPLE 4

The comparative example provides a method for detecting the moisturedamage of the asphalt pavement. The method detects the moisture damageof the asphalt pavement by using a Cifar10 model.

Contrastive analysis is performed on the embodiment 1, the comparativeexample 3 and the comparative example 4. FIG. 5 to FIG. 7 arecomparative results between the deep model and ACF (Aggregate ChannelFeatures) and Cifar10 and Ground Truth is a true value of the moisturedamage. It is found by comparison that the two comparative methods haveredundant detection regions or a lot of leak detection regions, and thecomparative results further verify the accuracy of the method.

Embodiment 2

The embodiment provides a method for adaptively selecting a groundpenetrating radar image for detecting a moisture damage. As shown in theFIG. 9, the method adaptively selects the GPR image according to acontrast of the GPR image, the method including the following steps:

S1, pre-processed GPR data is read:

GPR images with different contrasts are generated randomly in a setcontrast data range after pre-processing GPR data to construct aninitial random image data set, the initial random image data setincluding N pictures;

The method for acquiring the GPR data includes: acquiring field data ofthe asphalt pavement by using the GPR system, determining a damagedregion of the pavement with stripping or whitening in the field dataacquisition process and acquiring the GPR data corresponding to thedamaged region.

In a field data acquisition process, required by sampling parameters, asampling interval is smaller than 15 cm, an antenna frequency is greaterthan 1.6 GHz and a sampling frequency is 10-20 times of a main frequencyof an antenna.

Pre-processing is performed in a pre-processing course by adopting adirect current drift correction algorithm, a ground correctionalgorithm, a background deduction algorithm, a band-pass filteringalgorithm and a moving average algorithm.

The set contrast value range is 0.5-1.8.

N is equal to 100.

S2, resolutions of the pictures are adjusted:

It is found by researches that the images with different resolutions aredifferent in accuracy in the recognition model and the resolutions ofthe pictures affect the model recognition effect directly;

the initial random image data set is defined as an RID data set, the RIDdata set is zoomed to 224*224 and the data set is defined as a RBD dataset;

the resolution of the moisture damage initial image data set is zoomeddirectly to 224*224 to obtain the RBD data set;

S3, the data set is input into a recognition model:

the RBD data set acquired in the S2 is input into the recognition model,and S4 is executed after operation by the recognition model;

the picture input resolution size of the recognition model is 224*224and the picture output resolution size of the recognition model is224*224;

the recognition model is a mixed deep learning model, the mixed deeplearning model is comprised of two portions, feature extraction adoptsResNet50 and target detection adopts a YOLO V2 frame;

the ResNet50 and YOLO V2 frames are known deep learning models.

Feature extraction is comprised of four stages to achieve 16-time downsampling to convert the input 224*224 into 14*14*1024, thereby providingCNN original data to follow-up YOLO detection;

In the YOLO v2 frame, a target detection and candidate frame areprovided, YOLO Class Cony is provided with grids Grid=14*14, Anchorboxes=6.Loss function set by YOLO Transform is MSE.

The mixed deep learning model is divided into a training set and a testset by means of the images obtained in the S2, the distributionproportion being 70% and 30%. A specific model training method includestraining the designed mixed deep learning model by using a TL method.Loss function of the model uses a MSE method, and the number of Anchorboxes is acquired by classifying Height/Width ratios of the moisturedamage and the bridge joint of the sample set according to a K-meansmethod.

The mixed deep learning model uses three indexes: F1, Recall andPrecision to measure performance of the model.

S4, a moisture damage result is output:

the output result of the recognition model in the S3 is post-processed,post-processing including the steps:

S41, the quantity of candidate boxes BBoxes of images in the outputresult is judged, S42 is executed if the quantity of candidate boxesBBoxes is greater than 1, otherwise, the result directly is output;

S42, whether the candidate boxes BBoxes are overlapped or not arejudged, S43 is executed if the candidate boxes BBoxes are overlapped,otherwise, the result directly is output;

S43, whether label names corresponding to the overlapped candidate boxesare identical or not are judged, and if yes, the label namescorresponding to the merged candidate boxes being invariable and if no,indicating that moisture damage label names and bridge joint label namesare comprised simultaneously, the label names being output as bridgejoint, Joint;

S44, the candidate boxes are merged, the minimum value of intersectedcandidate boxes in x and y directions is taken, the maximum value of wand h is taken, and coordinates of the merged candidate boxes being[x_(min), y_(min), w_(max), h_(max)];

S45, the result is output, the output picture resolution is adjusted tobe equal to a picture resolution of the damage initial image data set inthe output result of the recognition model, the output result being thelabel name with a target and an image of the candidate box BBoxes (x, y,w, h) corresponding to the target;

S5, whether a detection target is present or not is judged with aninitial random image data set:

S51, the output result in the S4 is converted into a matrix A_(i)corresponding to pixel points in the picture, A_(i) being defined as:

${A_{i}\left\lbrack {m,n} \right\rbrack} = \left\{ {\begin{matrix}{1,\ } & {x_{i} \leq m \leq {x_{i} + {W_{i}\ {and}\ y_{i}}} \leq n \leq {y_{i} + h_{i}}} \\{0,\ } & {other}\end{matrix},} \right.$

where 1≤m≤H₀, 1≤n≤W₀

wherein H₀ is a picture height of the image output by the recognitionmodel and W₀ is a picture width of the image output by the recognitionmodel;

the matrixes A_(i) corresponding to the N pictures in the RID data setare summated and a mean value thereof is solved to acquire a mean valuematrix A, A being defined as

${A = {\frac{1}{N}{\sum\limits_{i = 1}^{N}A_{i}}}};$

S52, as the range of the contrast is optimized, the target is greatlydifferent from the background. If the tested GPR data contains thetarget, output results of most GPR images shall include the targetregion, and a mean value matrix A is large in value in the region. Ifthe tested GPR data is free of the target, fewer image corresponding toimproper contrasts have targets, and the mean value matrix A is small invalue in the region.

k₁=0.8 and θ₀=0.5 are set, and the mean value matrix A is updatedaccording to a formula below to acquire an updated mean value matrix A,

A(A<max(k* max(max(A)), θ₀))=0

wherein

k₁ is a target correlation coefficient for adjusting the maximum valueof the mean value so as to judge different targets;

θ₀ is the minimum value in the matrix A if the target is comprised, andif it is lower than the value, there is no target;

max(max(A)) is the maximum value in the mean value matrix A;

S53, a judging condition T for judging whether the target is present ornot is acquired according to a formula below on a basis of the updatedmean value matrix A, if T is equal to 1, indicating that a target ispresent and if T is equal to 0, indicating that no target is present;

$T = \left\{ {\begin{matrix}{1,\ } & {{{where}\ \max\left( {\max(A)} \right)} > 0} \\{0,\ } & {{{where}\ \max\left( {\max(A)} \right)} = 0}\end{matrix};} \right.$

S6, generating the GPR image randomly with increment method andselecting the image with a proper contrast:

when the picture contains the target, performing initial judgment;

S61, if Flag is equal to 0, indicating that the random image sample setis generated for the first time, i.e., an initial sample set stage, notentering follow-up selecting judgment, setting Flag=1, then adding 5% ofN pictures additionally as a sample of the random image data set, thetotal number of the pictures in the sample being N=(1+5%) N, andreturning to the S2;

S62, if Flag is not equal to 0, indicating a non-initial stage, settinga picture association coefficient, and selecting the picture with themaximum association coefficient of the mean value matrix A as the imagewith the proper contrast;

the association coefficient R_(i) is defined as

${R_{i} = \frac{\sum\limits_{m = 1}^{H_{0}}{\sum\limits_{n = 1}^{W_{0}}{\left( {{A\left( {m,n} \right)} - \mu_{A}} \right)\left( {{A_{i}\left( {m,n} \right)} - \mu_{A_{i}}} \right)}}}{\sqrt{\sum\limits_{m = 1}^{H_{0}}{\sum\limits_{n = 1}^{W_{0}}{\left( {{A\left( {m,n} \right)} - \mu_{A}} \right)^{2}{\sum\limits_{m = 1}^{H_{0}}{\sum\limits_{n = 1}^{W_{0}}\left( {{A_{i}\left( {m,n} \right)} - \mu_{A_{i}}} \right)^{2}}}}}}}};$

wherein R_(i) is an association coefficient between the matrix A_(i)corresponding to the i^(th) image and the mean value matrix A; m is acoordinate value in a height direction; n is a coordinate value in awidth direction; μ_(A) is a total mean value of the mean value matrix A;and μ_(A) _(i) is a total mean value of the matrix A_(i);

a termination condition of the selection process is as follows:

STOP = abs(F1 − F1_(pre)) < 0.01&&F1 > 0.8;${{F1} = {2*\frac{{Precision} \cdot {Recall}}{{Precision} + {Recall}}}};$${{Precision} = \frac{TP}{{TP} + {FP}}};$${{Recall} = \frac{TP}{{TP} + {FN}}};$

wherein F1 is an evaluation index of deep learning; F1 _(Pre) is anevaluation index of the previous deep learning, and is 0 initially; TPis a true target region; FP is a misrecognized true value, representingthat a unrecognized true value is judged as a negative value or abackground mistakenly; and FN is a misrecognized negative value, i.e.,the background is taken as the target; an index F1 calculated this timeis assigned to a variable F1 _(Pre) when the termination condition isnot met, and then returning to the S61 to increase the sample set so asto re-select;

the image with the proper contrast is output by the system when thetermination condition is met.

Effect Test Comparison:

By adopting the moisture damage image data set constructed manually, thedeep learning model is trained by using the YOLO detection frame and thetransfer learning and is recognized in combination with the algorithm inthe FIG. 9. GPR data is consistent with data in the FIG. 8. In order toobserve the change rule of indexes dependent on random sample number ofthe method, the initial sample set in the FIG. 9 is N=20, the value ofthe contrast is 0.6-1.8, the parameter k₁=0.8, θ₀=0.5 in the matrix A isupdated, and N is equal to 100 in actual application.

FIG. 10 is a change rule of the correlation coefficient dependent on thesampled sample number, wherein Referenced F1 is a F1 index, and theother two curves are correlation coefficients between the preferredimage and the mean value A and the true value (Ground Truth). It can beknown from the figure that with increase of the sample number, thecorrelation coefficients R are increased and is stabilized at a fixedvalue and are not increased along with increase of the sample number,showing that the algorithm has found the preferred GPR image.

FIG. 11 is a change rule of the deep model related index dependent onthe sampled sample number, F1, Precision and Recall are evaluationindexes of the deep model. Similarly, after the preferred result isfound, the indexes are stable, showing that a proper image is found.

FIG. 12 is a heatmap for overall detection of the random data set with160 random sample sets overlapped on the preferred image. It can beknown that in the region corresponding to 103.5 m, 160 pictures havemoisture damages (target) results at the position and are consistentwith the true value (1-4 in the FIG. 8) region. The three indexes aregreater than 0.93, showing that the preferred image is very close to thetrue value and the correctness of the algorithm is verified.

FIG. 13 is a heatmap of the mean value A overlapped on the preferredresult (i.e., all detection results in the random image data set areaccumulated and the size of the accumulated results is reflected bycolor). As the mean value A takes out all the results below maximumvalue*0.8 in the matrix A in the updating process and the results areaverage values. It can be known by comparing the detection result withthe Ground Truth that the goodness of fit is very high, and thecorrectness of the algorithm is further verified.

FIG. 14 is a heatmap of overall detection of the normal pavement on therandom data set. As the size of the initialized sample number is 100 andthe maximum value in the figure is only 28 (the mean value is 0.28 aftercalculation) and is smaller than a threshold value of θ₀=0.5). The testdata is classified into the normal pavement, which is consistent withactual condition.

FIG. 15 is a distribution rule of the random sample number of the testdata set. 31 samples (11 normal pavements and 20 moisture damagesamples) are tested by the algorithm, showing that the algorithmclassifies the 11 normal pavements effectively, wherein the number ofused samples is 100; the random samples needed by the moisture damageregion is mainly focused between 200 and 300. As 95% of GPR data forpavement investigation are normal pavements, the algorithm can save thecalculating cost effectively.

In order to further describe the effectiveness of the method (anincremental sampling method, marked as IRS) and compare the result ofrandom selection method (RS), the FIG. 16 is a comparison result fordata sets (20) with moisture damages and FIG. 17 increases 11 normalpavement results. It can be known form the result that the IRS methodcan recognize the defects effectively and is higher in precision.

It is shown by the experiment that the incremental sampling method andthe deep model are combined in use, such that the radar spectra withproper contrasts can be selected from the GPR original data effectively,thereby providing an effective method for automatic application of GPR.

Although the method verifies recognition of the moisture damage defects,the method is not limited to the case. Recognition of the targets inother radar spectra by the method shall fall within the scope ofprotection of the present invention.

Embodiment 3

The embodiment provides a method for detecting the moisture damage ofthe asphalt pavement based on adaptive selection of gray levels ofimages. As shown in the FIG. 1 to FIG. 17, the method is substantiallyas same as the method for detecting the moisture damage of the asphaltpavement of the embodiment 1. The difference is merely replacing settingthe contrast of the GPR image and intercepting the GPR image at a lengthof 5-6 m with selecting the GPR image with a proper contrast andintercepting the GPR image at a length of 5-6 m in the S12.

The selection method for the GPR image with the proper contrast is theadaptive selection method for the GPR image;

the adaptive selection method for the ground penetrating image is assame as the method for detecting the moisture damage of the asphaltpavement in the embodiment 2.

The recognition models in the embodiment 1 and the embodiment 2 aresame, and the post-processing steps in the embodiment 1 and theembodiment 2 are same.

The method of the embodiment can optimize the image for each GPR dataand input the optimized image to the deep model to obtain the detectionresult. The method solves the problem of image optimization and imagerecognition of the moisture damage, thereby truly achieving automaticand intelligent work on moisture damage defect detection.

What is claimed is:
 1. A method for adaptively selecting a GroundPenetrating Radar (GPR) image for detecting a moisture damage, whereinthe method adaptively selects the GPR image with a proper contrastaccording to data of the GPR image, comprising the following steps: S1,reading pre-processed GPR data: generating GPR images with differentcontrasts randomly in a set contrast data range after pre-processing GPRdata to construct an initial random image data set, wherein the initialrandom image data set comprising N pictures; S2, adjusting resolutionsof the N pictures: defining the initial random image data set as an RIDdata set, zooming the RID data set to 224*224 to obtain a zoomed dataset and defining the zoomed data set as an RBD data set; zooming aresolution of a moisture damage initial image data set directly to224*224 to obtain the RBD data set; S3, inputting the RBD data set intoa recognition model: inputting the RBD data set obtained in the step S2into the recognition model, and executing a step S4 after an operationof the recognition model, wherein a picture input resolution size of therecognition model is 224*224 and a picture output resolution size of therecognition model is 224*224; the recognition model is a mixed deeplearning model, wherein the mixed deep learning model is comprised oftwo portions: a feature extraction adopting ResNet50 and a targetdetection adopting a YOLO V2 frame; S4, outputting a moisture damageresult: post-processing an output result of the recognition model in theS3, wherein the post-processing comprising the following steps: S41,judging a quantity of candidate boxes BBoxes of a spectra in the outputresult, executing S42 when the quantity of candidate boxes BBoxes isgreater than 1, otherwise, outputting a result directly; S42, judgingwhether the candidate boxes BBoxes are overlapped or not, executing S43when the candidate boxes BBoxes are overlapped, otherwise, outputtingthe result directly; S43, judging whether label names corresponding tooverlapped candidate boxes are identical or not, wherein when labelnames corresponding to the overlapped candidate boxes are identical, thelabel names corresponding to merged candidate boxes are invariable andwhen label names corresponding to the overlapped candidate boxes are notidentical, indicating that moisture damage label names and bridge jointlabel names are comprised simultaneously, the label names are output asbridge joint, Joint; S44, merging the candidate boxes BBoxes, taking aminimum value of intersected candidate boxes in x and y directions,taking a maximum value of w and h, wherein coordinates of a mergedcandidate box are [x_(min), y_(min), w_(max), h_(max)]; S45, outputtingthe result, adjusting an output picture resolution to be equal to apicture resolution of the moisture damage initial image data set in theoutput result of the recognition model, wherein the output result is alabel name with a target and an image of the candidate box BBoxes (x, y,w, h) corresponding to the target; S5, judging whether a detectiontarget is present or not with the initial random image data set: S51,converting the output result in the S4 into a matrix A_(i) correspondingto pixel points in a picture, wherein A_(i) is defined as:${A_{i}\left\lbrack {m,n} \right\rbrack} = \left\{ {\begin{matrix}{1,\ } & {x_{i} \leq m \leq {x_{i} + {W_{i}\ {and}\ y_{i}}} \leq n \leq {y_{i} + h_{i}}} \\{0,\ } & {other}\end{matrix},} \right.$ where 1≤m≤H₀, 1≤n≤W₀; wherein H₀ is a pictureheight of an image output by the recognition model and W₀ is a picturewidth of the image output by the recognition model; summating thematrixes A_(i) corresponding to the N pictures in the RID data set andcalculating a mean value of the matrixes A_(i) to acquire a mean valuematrix A, wherein A is defined as:${A = {\frac{1}{N}{\sum\limits_{i = 1}^{N}A_{i}}}};$ S52, setting k₁=0.8and θ₀=0.5, and updating the mean value matrix A according to a formulabelow to acquire an updated mean value matrix A;A(A<max(k ₁*max(max(A)), θ₀))=0; wherein k₁ is a target associationcoefficient; θ₀ is a minimum value in the matrix A when the target iscomprised, and no target is present when the mean value is lower thanthe minimum value; max(max(A)) is a maximum value in the mean valuematrix A; S53, acquiring a judging condition T for judging whether thetarget is present or not according to a formula below on a basis of theupdated mean value matrix A, and when the judging condition T is equalto 1, indicating that the target is present and when the judgingcondition T is equal to 0, indicating that no target is present;$T = \left\{ {\begin{matrix}{1,\ } & {{{where}\ \max\left( {\max(A)} \right)} > 0} \\{0,\ } & {{{where}\ \max\left( {\max(A)} \right)} = 0}\end{matrix};} \right.$ and S6, generating the GPR image randomlyincrementally and selecting the GPR image with the proper contrast: whenthe picture contains the target, performing an initial judgment; S61,when Flag is equal to 0, indicating that a random image sample set isgenerated for a first time in an initial sample set stage, not enteringa follow-up selecting judgment, setting Flag=1, then adding 5% of the Npictures additionally as a sample of the initial random image data set,a total number of the N pictures in the sample being is N=(1+5%) N, andreturning to the S2; S62, when the Flag is not equal to 0, indicating anon-initial stage, setting a picture association coefficient, andselecting the picture with a maximum association coefficient of the meanvalue matrix A as the GPR image with the proper contrast; the pictureassociation coefficient R_(i) is defined as:${R_{i} = \frac{\sum\limits_{m = 1}^{H_{0}}{\sum\limits_{n = 1}^{W_{0}}{\left( {{A\left( {m,n} \right)} - \mu_{A}} \right)\left( {{A_{i}\left( {m,n} \right)} - \mu_{A_{i}}} \right)}}}{\sqrt{\sum\limits_{m = 1}^{H_{0}}{\sum\limits_{n = 1}^{W_{0}}{\left( {{A\left( {m,n} \right)} - \mu_{A}} \right)^{2}{\sum\limits_{m = 1}^{H_{0}}{\sum\limits_{n = 1}^{W_{0}}\left( {{A_{i}\left( {m,n} \right)} - \mu_{A_{i}}} \right)^{2}}}}}}}};$wherein R_(i) is the picture association coefficient between the matrixA_(i) corresponding to an i^(th) image and the mean value matrix A; m isa coordinate value in a height direction; n is a coordinate value in awidth direction; μ_(A) is a total mean value of the mean value matrix A;and μ_(A) _(i) is a total mean value of the matrix A_(i); a terminationcondition of a selection process is as follows:STOP = abs(F1 − F1_(pre)) < 0.01&&F1 > 0.8;${{F1} = {2*\frac{{Precision} \cdot {Recall}}{{Precision} + {Recall}}}};$${{Precision} = \frac{TP}{{TP} + {FP}}};$${{Recall} = \frac{TP}{{TP} + {FN}}};$ wherein F1 is an evaluation indexof a deep learning; F1 _(Pre) is an evaluation index of a previous deeplearning, and is 0 initially; TP is a true target region; FP is amisrecognized true value, representing that an unrecognized true valueis judged as a negative value or a background mistakenly; and FN is amisrecognized negative value wherein the background is taken as thetarget; assigning the evaluation index F1 calculated to a variable F1_(Pre) when the termination condition is not met, and then returning tothe step S61 to increase the random image sample set so as to re-select;outputting the GPR image with the proper contrast by a GPR system whenthe termination condition is met.
 2. The method according to claim 1,wherein acquiring the GPR data comprises: acquiring field data of anasphalt pavement by using the GPR system, determining a damaged regionof the asphalt pavement with pumping or whitening in a field dataacquisition process and acquiring the GPR data corresponding to thedamaged region.
 3. The method according to claim 1, wherein during afield data acquisition process, sampling parameter requirements comprisea sampling interval smaller than 15 cm, an antenna frequency greaterthan 1.6 GHz and a sampling frequency 10-20 times of a main frequency ofan antenna.
 4. The method according to claim 1, wherein pre-processingis a course of adopting a direct current drift connection algorithm, aground correction algorithm, a background deduction algorithm, aband-pass filtering algorithm and performing a sliding average algorithmto perform the pre-processing.
 5. The method according to claim 1,wherein the set contrast data range is 0.5-1.8.
 6. The method accordingto claim 1, wherein N is equal to 100.